Mining Association Rules in Temporal Document Collections

  • Kjetil Nørvåg
  • Trond Øivind Eriksen
  • Kjell-Inge Skogstad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


In this paper we describe how to mine association rules in temporal document collections. We describe how to perform the various steps in the temporal text mining process, including data cleaning, text refinement, temporal association rule mining and rule post-processing. We also describe the Temporal Text Mining Testbench, which is a user-friendly and versatile tool for performing temporal text mining, and some results from using this tool.


Association Rule Rule Mining Association Rule Mining Stop Word Proper Noun 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chakrabarti, S.: Mining the Web - Discovering Knowledge from Hypertext Data. Morgan Kaufmann Publishers, San Francisco (2003)Google Scholar
  2. 2.
    Dunham, M.: Data Mining: Introductory and Advanced Topics. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  3. 3.
    Holt, J.D., Chung, S.M.: Efficient mining of association rules in text databases. In: Proceedings of CIKM 1999 (1999)Google Scholar
  4. 4.
    Janetzko, D., Cherfi, H., Kennke, R., Napoli, A., Toussaint, Y.: Knowledge-based selection of association rules for text mining. In: Proceedings of ECAI 2004 (2004)Google Scholar
  5. 5.
    Lee, C.-H., Lin, C.-R., Chen, M.-S.: On mining general temporal association rules in a publication database. In: Proceedings of ICDM 2001 (2001)Google Scholar
  6. 6.
    Lent, B., Agrawal, R., Srikant, R.: Discovering trends in text databases. In: Proceedings of KDD 1997 (1997)Google Scholar
  7. 7.
    Lu, H., Feng, L., Han, J.: Beyond intratransaction association analysis: mining multidimensional intertransaction association rules. ACM Trans. Inf. Syst. 18(4), 423–454 (2000)CrossRefGoogle Scholar
  8. 8.
    Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: Proceedings of KDD 2005 (2005)Google Scholar
  9. 9.
    Nørvåg, K.: Supporting temporal text-containment queries in temporal document databases. Journal of Data & Knowledge Engineering 49(1), 105–125 (2004)CrossRefGoogle Scholar
  10. 10.
    Roddick, J.F., Spiliopoulou, M.: Survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14(4), 750–767 (2002)CrossRefGoogle Scholar
  11. 11.
    Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of KDD 2002 (2002)Google Scholar
  12. 12.
    Tung, A.K.H., Lu, H., Han, J., Feng, L.: Efficient mining of intertransaction association rules. IEEE Transactions on Knowledge and Data Engineering 15(1), 43–56 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kjetil Nørvåg
    • 1
  • Trond Øivind Eriksen
    • 1
  • Kjell-Inge Skogstad
    • 1
  1. 1.Dept. of Computer and Information ScienceNTNUTrondheimNorway

Personalised recommendations